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Use ruff for formatting

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Lars Bogner
2025-11-05 09:58:30 +01:00
parent 6cec127797
commit e56111be81
2 changed files with 7 additions and 4 deletions
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notebooks/01_simple-tests.ipynb
+5 -4
View File
@@ -260,6 +260,7 @@
"X_train_scaled, X_test_scaled = datamanip.scale_data(x_train, x_test)\n", "X_train_scaled, X_test_scaled = datamanip.scale_data(x_train, x_test)\n",
"y_train_scaled, y_test_scaled = datamanip.scale_data(y_train, y_test)\n", "y_train_scaled, y_test_scaled = datamanip.scale_data(y_train, y_test)\n",
"\n", "\n",
"\n",
"def get_regression_model(\n", "def get_regression_model(\n",
" n_hidden_layers: int, n_neurons: int, activation: type = LeakyReLU\n", " n_hidden_layers: int, n_neurons: int, activation: type = LeakyReLU\n",
") -> list[Layer]:\n", ") -> list[Layer]:\n",
@@ -383,7 +384,7 @@
"beta = optimizers.Ridge_parameters(X_train_scaled, y_train_scaled, lam=1e-10)\n", "beta = optimizers.Ridge_parameters(X_train_scaled, y_train_scaled, lam=1e-10)\n",
"y_pred = X_test_scaled @ beta\n", "y_pred = X_test_scaled @ beta\n",
"test_mse = mean_squared_error(y_test_scaled, y_pred)\n", "test_mse = mean_squared_error(y_test_scaled, y_pred)\n",
"print(f\"OLS Test MSE: {test_mse}\")\n" "print(f\"OLS Test MSE: {test_mse}\")"
] ]
}, },
{ {
@@ -406,9 +407,9 @@
"source": [ "source": [
"import plotting\n", "import plotting\n",
"\n", "\n",
"plt.scatter(x_test[:,1], y_test_scaled, s=6, label=\"True Data\")\n", "plt.scatter(x_test[:, 1], y_test_scaled, s=6, label=\"True Data\")\n",
"plt.scatter(x_test[:,1], y_pred, s=6, label=\"OLS Prediction\")\n", "plt.scatter(x_test[:, 1], y_pred, s=6, label=\"OLS Prediction\")\n",
"plt.scatter(x_test[:,1], y_pred_nn, s=6, label=\"NN Prediction\")\n", "plt.scatter(x_test[:, 1], y_pred_nn, s=6, label=\"NN Prediction\")\n",
"plt.legend()\n", "plt.legend()\n",
"plt.xlabel(\"$x$\")\n", "plt.xlabel(\"$x$\")\n",
"plt.ylabel(r\"$y / \\sigma_y$\")\n", "plt.ylabel(r\"$y / \\sigma_y$\")\n",
src/easynn/feedforward.py
+2
View File
@@ -55,6 +55,7 @@ class Softmax(ActivationFunction):
s = self.forward(values) s = self.forward(values)
return s * (1 - s) return s * (1 - s)
class Sigmoid(ActivationFunction): class Sigmoid(ActivationFunction):
def forward(self, values: np.ndarray) -> np.ndarray: def forward(self, values: np.ndarray) -> np.ndarray:
return 1 / (1 + np.exp(-values)) return 1 / (1 + np.exp(-values))
@@ -63,6 +64,7 @@ class Sigmoid(ActivationFunction):
sig = self.forward(values) sig = self.forward(values)
return sig * (1 - sig) return sig * (1 - sig)
# === Loss Functions === # === Loss Functions ===